Field of the Invention
The present invention relates to the field of computer software systems that make recommendations.
Background of the Invention
Recommendation systems have become ubiquitous in the Digital Age—commercial websites recommend products and services to purchase, media websites recommend content to view, social websites recommend people to meet, etc. To make these recommendations, the systems collect information—implicit data such as purchasing patterns, and explicit data such as ratings. These data are analyzed by the system to make targeted recommendations to users. To collect ratings on a product, for example, a typical system may ask prior buyers to give a numerical rating and a written review of the product. The reviewers, however, are not told to rate with any particular consumer in mind, or are merely asked to rate their own personal experiences. This style of user review works well for objective questions, but fails miserably for subjective questions, especially for polarizing items.
As an example, consider a tool that is clumsy for right-handed people, but a godsend for left-handed people. In this case, any rating is inherently nonsensical without knowing the handedness of the hypothetical target. If users are asked to rate on behalf of themselves, the system will receive a mix of both extremes. Combining these ratings results in a middling average, which is not representative of either set of users. If users are asked to rate on behalf of others in general, they may have a dilemma. Even a right-handed user who despises the tool may recognize its utility for left-handed people. It is impossible to accurately express the evaluation in a one-size-fits-all numerical rating. As a result, reviewers are forced to make written reviews to express any caveats, which then must be laboriously traversed by readers. What is needed is a system that collects ratings from users with the needs and unique attributes of a particular subject in mind.
Another issue in recommendation systems is establishing trust in the reviews. When the reviewers are unknown to the consumer, it is difficult for the system to convince the consumer to trust the reviews. Some recommendation systems have to contend with fake reviews or paid reviewers. Even if reviewers are verified, reviewers always differ in expertise and familiarity with the product. Therefore, users are often not able to rely on average ratings, and are forced to sift through each product listing and read the lengthy review text. What is needed is a system where reviewers are known to the person searching for a product.